Traditional traffic control systems such as Sydney Coordinated Adaptive Traffic System (SCATS) rely on detectors installed under the pavement to provide traffic feedback for adaptive control of green split. Installation of such detectors are usually expensive. In addition, these detectors are often malfunctioned, resulting in erroneous signals. In some cases, signals from certain detectors are even absent. To enhance the robustness of the detector-based traditional traffic systems, traffic control plans, such as green split plans, are often designed to be very similar to each other, and the conditions for initiating plan change are usually conservatively set, resulting in a nearly-fixed green split regardless of actual traffic conditions, thereby greatly diminishing the benefit of adaptivity.
Embodiments of the disclosure improve the traditional system by utilizing vehicle trajectory data, which are not traditionally used in designing and/or operating traffic control systems. Vehicle trajectory data have become available as a viable information source thanks to the proliferation of app-based ride hailing and ride sharing services, where vehicle trajectory data can be collected based on, for example, vehicle positioning information and map information. Utilizing vehicle trajectory data for optimizing traffic control plans provides an efficient new approach for adaptively responding to traffic conditions.